# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
from typing import Any, Dict, List, Optional, Tuple, Union

import paddle
import paddle.nn as nn
from paddle.distributed.fleet.utils import recompute

from ..configuration_utils import ConfigMixin, register_to_config

# from ..loaders import FromOriginalModelMixin, PeftAdapterMixin
from ..models.attention import JointTransformerBlock
from ..models.attention_processor import Attention, AttentionProcessor
from ..models.modeling_utils import ModelMixin
from ..models.normalization import AdaLayerNormContinuous
from ..utils import (
    USE_PEFT_BACKEND,
    logging,
    recompute_use_reentrant,
    scale_lora_layers,
    unscale_lora_layers,
    use_old_recompute,
)
from .embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from .simplified_sd3 import SimplifiedSD3
from .transformer_2d import Transformer2DModelOutput

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

import paddle.distributed.fleet as fleet


class SD3Transformer2DModel(ModelMixin, ConfigMixin):  # , PeftAdapterMixin, FromOriginalModelMixin
    """
    The Transformer model introduced in Stable Diffusion 3.
    Reference: https://arxiv.org/abs/2403.03206
    Parameters:
        sample_size (`int`): The width of the latent images. This is fixed during training since
            it is used to learn a number of position embeddings.
        patch_size (`int`): Patch size to turn the input data into small patches.
        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
        num_layers (`int`, *optional*, defaults to 18): The number of layers of Transformer blocks to use.
        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        caption_projection_dim (`int`): Number of dimensions to use when projecting the `encoder_hidden_states`.
        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
        out_channels (`int`, defaults to 16): Number of output channels.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: int = 128,
        patch_size: int = 2,
        in_channels: int = 16,
        num_layers: int = 18,
        attention_head_dim: int = 64,
        num_attention_heads: int = 18,
        joint_attention_dim: int = 4096,
        caption_projection_dim: int = 1152,
        pooled_projection_dim: int = 2048,
        out_channels: int = 16,
        pos_embed_max_size: int = 96,
        dual_attention_layers: Tuple[
            int, ...
        ] = (),  # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
        qk_norm: Optional[str] = None,
    ):
        super().__init__()
        default_out_channels = in_channels
        self.out_channels = out_channels if out_channels is not None else default_out_channels
        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim

        self.pos_embed = PatchEmbed(
            height=self.config.sample_size,
            width=self.config.sample_size,
            patch_size=self.config.patch_size,
            in_channels=self.config.in_channels,
            embed_dim=self.inner_dim,
            pos_embed_max_size=pos_embed_max_size,  # hard-code for now.
        )
        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
            embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
        )
        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.config.caption_projection_dim)

        self.inference_optimize = os.getenv("INFERENCE_OPTIMIZE") == "True"
        self.inference_mp_size = int(os.getenv("INFERENCE_MP_SIZE", 1))
        self.inference_dp_size = int(os.getenv("INFERENCE_DP_SIZE", 1))
        self.mp_id = 0
        self.dp_id = 0
        if self.inference_mp_size > 1 or self.inference_dp_size > 1:
            assert self.inference_dp_size in [1, 2]
            hcg = fleet.get_hybrid_communicate_group()
            self.mp_id = hcg.get_model_parallel_rank()
            self.dp_id = hcg.get_data_parallel_rank()

            mp_degree = hcg.get_model_parallel_world_size()
            dp_degree = hcg.get_data_parallel_world_size()
            assert mp_degree == self.inference_mp_size
            assert dp_degree == self.inference_dp_size

        # `attention_head_dim` is doubled to account for the mixing.
        # It needs to crafted when we get the actual checkpoints.
        self.transformer_blocks = nn.LayerList(
            [
                JointTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.inner_dim,
                    context_pre_only=i == num_layers - 1,
                    qk_norm=qk_norm,
                    use_dual_attention=True if i in dual_attention_layers else False,
                )
                for i in range(self.config.num_layers)
            ]
        )
        if self.inference_optimize:
            # we do not need self.transformer_blocks, del it to save memory.
            del self.transformer_blocks
            self.simplified_sd3 = SimplifiedSD3(
                num_layers,
                dim=self.inner_dim,
                num_attention_heads=self.config.num_attention_heads,
                attention_head_dim=self.inner_dim,
                mp_degree=self.inference_mp_size,
            )

        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias_attr=True)

        self.gradient_checkpointing = False

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
        """
        Sets the attention processor to use [feed forward
        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
        Parameters:
            chunk_size (`int`, *optional*):
                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
                over each tensor of dim=`dim`.
            dim (`int`, *optional*, defaults to `0`):
                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
                or dim=1 (sequence length).
        """
        if dim not in [0, 1]:
            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

        # By default chunk size is 1
        chunk_size = chunk_size or 1

        def fn_recursive_feed_forward(module: paddle.nn.Layer, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, chunk_size, dim)

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: paddle.nn.Layer, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.
        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.
                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.
        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: paddle.nn.Layer, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
        are fused. For cross-attention modules, key and value projection matrices are fused.
        <Tip warning={true}>
        This API is 🧪 experimental.
        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.
        <Tip warning={true}>
        This API is 🧪 experimental.
        </Tip>
        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    def sd3_origin_transformer(
        self,
        hidden_states,
        encoder_hidden_states,
        temb,
        block_controlnet_hidden_states: List = None,
    ):
        for index_block, block in enumerate(self.transformer_blocks):
            if self.training and self.gradient_checkpointing and not use_old_recompute():

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs = {} if recompute_use_reentrant() else {"use_reentrant": False}
                hidden_states = recompute(
                    create_custom_forward(block),
                    hidden_states,
                    encoder_hidden_states,
                    temb,
                    **ckpt_kwargs,
                )
            else:
                encoder_hidden_states, hidden_states = block(
                    hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
                )
            
            # controlnet residual
            if block_controlnet_hidden_states is not None and block.context_pre_only is False:
                interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
                hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]
        return encoder_hidden_states, hidden_states

    def forward(
        self,
        hidden_states: paddle.Tensor,
        encoder_hidden_states: paddle.Tensor = None,
        pooled_projections: paddle.Tensor = None,
        timestep: paddle.Tensor = None,
        block_controlnet_hidden_states: List = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
    ) -> Union[paddle.Tensor, Transformer2DModelOutput]:
        """
        The [`SD3Transformer2DModel`] forward method.
        Args:
            hidden_states (`paddle.Tensor` of shape `(batch size, channel, height, width)`):
                Input `hidden_states`.
            encoder_hidden_states (`paddle.Tensor` of shape `(batch size, sequence_len, embed_dims)`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
            pooled_projections (`paddle.Tensor` of shape `(batch_size, projection_dim)`): Embeddings projected
                from the embeddings of input conditions.
            timestep ( `paddle.Tensor`):
                Used to indicate denoising step.
            block_controlnet_hidden_states: (`list` of `paddle.Tensor`):
                A list of tensors that if specified are added to the residuals of transformer blocks.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                tuple.
        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """

        if joint_attention_kwargs is not None:
            joint_attention_kwargs = joint_attention_kwargs.copy()
            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)
        else:
            logger.debug(
                "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
            )

        height, width = hidden_states.shape[-2:]

        hidden_states = self.pos_embed(hidden_states)  # takes care of adding positional embeddings too.
        temb = self.time_text_embed(timestep, pooled_projections)
        encoder_hidden_states = self.context_embedder(encoder_hidden_states)

        if self.inference_optimize:
            hidden_states = self.simplified_sd3(
                hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb
            )
            encoder_hidden_states = None
        else:
            encoder_hidden_states, hidden_states = self.sd3_origin_transformer(
                hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=temb, block_controlnet_hidden_states=block_controlnet_hidden_states
            )

        hidden_states = self.norm_out(hidden_states, temb)
        hidden_states = self.proj_out(hidden_states)

        # unpatchify
        patch_size = self.config.patch_size
        height = height // patch_size
        width = width // patch_size

        hidden_states = hidden_states.reshape(
            shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
        )

        hidden_states = paddle.transpose(hidden_states, [0, 5, 1, 3, 2, 4])
        output = hidden_states.reshape(
            shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
        )

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)

    @classmethod
    def custom_modify_weight(cls, model_to_load, state_dict):

        if not model_to_load.inference_optimize:
            return

        # NOTE:(changwenbin,zhoukangkang) SD3 num_layers is 24
        sd3_num_layers = 24
        for i in range(sd3_num_layers):
            base_map_sd3 = [
                (f"linear1.{i}.weight", f"{i}.norm1.linear.weight"),
                (f"linear1.{i}.bias", f"{i}.norm1.linear.bias"),
                (f"linear_context.{i}.weight", f"{i}.norm1_context.linear.weight"),
                (f"linear_context.{i}.bias", f"{i}.norm1_context.linear.bias"),
                (f"q.{i}.weight", f"{i}.attn.to_q.weight"),
                (f"q.{i}.bias", f"{i}.attn.to_q.bias"),
                (f"k.{i}.weight", f"{i}.attn.to_k.weight"),
                (f"k.{i}.bias", f"{i}.attn.to_k.bias"),
                (f"v.{i}.weight", f"{i}.attn.to_v.weight"),
                (f"v.{i}.bias", f"{i}.attn.to_v.bias"),
                (f"ek.{i}.weight", f"{i}.attn.add_k_proj.weight"),
                (f"ek.{i}.bias", f"{i}.attn.add_k_proj.bias"),
                (f"ev.{i}.weight", f"{i}.attn.add_v_proj.weight"),
                (f"ev.{i}.bias", f"{i}.attn.add_v_proj.bias"),
                (f"eq.{i}.weight", f"{i}.attn.add_q_proj.weight"),
                (f"eq.{i}.bias", f"{i}.attn.add_q_proj.bias"),
                (f"to_out_linear.{i}.weight", f"{i}.attn.to_out.0.weight"),
                (f"to_out_linear.{i}.bias", f"{i}.attn.to_out.0.bias"),
                (f"ffn1.{i}.weight", f"{i}.ff.net.0.proj.weight"),
                (f"ffn1.{i}.bias", f"{i}.ff.net.0.proj.bias"),
                (f"ffn2.{i}.weight", f"{i}.ff.net.2.weight"),
                (f"ffn2.{i}.bias", f"{i}.ff.net.2.bias"),
            ]
            if i < sd3_num_layers - 1:
                extra_map_sd3 = [
                    (f"to_add_out_linear.{i}.weight", f"{i}.attn.to_add_out.weight"),
                    (f"to_add_out_linear.{i}.bias", f"{i}.attn.to_add_out.bias"),
                    (f"ffn1_context.{i}.weight", f"{i}.ff_context.net.0.proj.weight"),
                    (f"ffn1_context.{i}.bias", f"{i}.ff_context.net.0.proj.bias"),
                    (f"ffn2_context.{i}.weight", f"{i}.ff_context.net.2.weight"),
                    (f"ffn2_context.{i}.bias", f"{i}.ff_context.net.2.bias"),
                ]
            map_sd3 = base_map_sd3 + extra_map_sd3

            for to_, from_ in map_sd3:
                if "transformer_blocks." + from_ in state_dict:
                    state_dict["simplified_sd3." + to_] = state_dict["transformer_blocks." + from_]
                else:
                    print(f"Warning!!: '{from_}' not found in state_dict")

            # concat qkv weight and bias.
            for placeholder1 in ["", "e"]:
                for placeholder2 in ["weight", "bias"]:
                    state_dict[f"simplified_sd3.{placeholder1}qkv.{i}.{placeholder2}"] = paddle.concat(
                        [
                            state_dict[f"simplified_sd3.{placeholder1}q.{i}.{placeholder2}"],
                            state_dict[f"simplified_sd3.{placeholder1}k.{i}.{placeholder2}"],
                            state_dict[f"simplified_sd3.{placeholder1}v.{i}.{placeholder2}"],
                        ],
                        axis=-1,
                    )

            mp_degree = model_to_load.inference_mp_size
            mp_id = model_to_load.mp_id

            if mp_degree > 1:
                if i < 23:
                    tmp = paddle.split(state_dict[f"simplified_sd3.to_add_out_linear.{i}.weight"], mp_degree, axis=0)
                    state_dict[f"simplified_sd3.to_add_out_linear_mp.{i}.weight"] = tmp[mp_id]
                    state_dict[f"simplified_sd3.to_add_out_linear_mp.{i}.bias"] = state_dict[
                        f"simplified_sd3.to_add_out_linear.{i}.bias"
                    ]
                    tmp = paddle.split(state_dict[f"simplified_sd3.ffn2_context.{i}.weight"], mp_degree, axis=0)
                    state_dict[f"simplified_sd3.ffn2_context_mp.{i}.weight"] = tmp[mp_id]
                    state_dict[f"simplified_sd3.ffn2_context_mp.{i}.bias"] = state_dict[
                        f"simplified_sd3.ffn2_context.{i}.bias"
                    ]
                    for placeholder in ["weight", "bias"]:
                        tmp = paddle.split(
                            state_dict[f"simplified_sd3.ffn1_context.{i}.{placeholder}"], mp_degree, axis=-1
                        )
                        state_dict[f"simplified_sd3.ffn1_context_mp.{i}.{placeholder}"] = tmp[mp_id]
                for placeholder in ["weight", "bias"]:
                    tmp = paddle.split(state_dict[f"simplified_sd3.ffn1.{i}.{placeholder}"], mp_degree, axis=-1)
                    state_dict[f"simplified_sd3.ffn1_mp.{i}.{placeholder}"] = tmp[mp_id]
                    for placeholder1 in ["", "e"]:
                        tmp = paddle.split(
                            state_dict[f"simplified_sd3.{placeholder1}qkv.{i}.{placeholder}"], 3 * mp_degree, axis=-1
                        )
                        state_dict[f"simplified_sd3.{placeholder1}qkv_mp.{i}.{placeholder}"] = paddle.concat(
                            [tmp[mp_id], tmp[1 * mp_degree + mp_id], tmp[2 * mp_degree + mp_id]], axis=-1
                        )
                for mp_name in ["ffn2", "to_out_linear"]:
                    tmp = paddle.split(state_dict[f"simplified_sd3.{mp_name}.{i}.weight"], mp_degree, axis=0)
                    state_dict[f"simplified_sd3.{mp_name}_mp.{i}.weight"] = tmp[mp_id]
                    state_dict[f"simplified_sd3.{mp_name}_mp.{i}.bias"] = state_dict[
                        f"simplified_sd3.{mp_name}.{i}.bias"
                    ]
